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                        Introduction
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                            <b>1.</b>
                        
                        第一章：数据分析前奏
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                            <b>1.1.</b>
                        
                        第1节：什么是数据分析
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                            <b>1.2.</b>
                        
                        第2节：环境搭建
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                            <b>1.3.</b>
                        
                        第3节：jupyternotebook使用
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                            <b>1.4.</b>
                        
                        第4节：作业
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                            <b>2.</b>
                        
                        第二章：Numpy库
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                            <b>2.1.</b>
                        
                        第1节：numpy库介绍
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                            <b>2.2.</b>
                        
                        第2节：numpy数组基本
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                            <b>2.3.</b>
                        
                        第3节：numpy数组操作
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                            <b>2.4.</b>
                        
                        第4节：numpy索引和切片
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                            <b>2.5.</b>
                        
                        第5节：Numpy索引和切片作业
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                            <b>2.6.</b>
                        
                        第6节：numpy数组操作
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                            <b>2.7.</b>
                        
                        第7节：深拷贝和浅拷贝
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                            <b>2.8.</b>
                        
                        第8节：文件操作
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                            <b>2.9.</b>
                        
                        第9节：数组操作和文件操作作业.md
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                            <b>2.10.</b>
                        
                        第10节：NAN和INF值处理.md
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                            <b>2.11.</b>
                        
                        第11节：random模块.md
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                            <b>2.12.</b>
                        
                        第12节：axis理解.md
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                            <b>2.13.</b>
                        
                        第13节：通用函数.md
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                            <b>2.14.</b>
                        
                        第14节：numpy练习题.md
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                            <b>3.</b>
                        
                        第三章：Pandas库
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                            <b>3.1.</b>
                        
                        第1节：pandas介绍
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                            <b>3.2.</b>
                        
                        第2节：pandas索引操作
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                            <b>3.3.</b>
                        
                        第3节：pandas对齐运算
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                            <b>3.4.</b>
                        
                        第4节：pandas函数应用
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                            <b>3.5.</b>
                        
                        第5节：pandas层级索引
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                            <b>3.6.</b>
                        
                        第6节：pandas统计计算和描述
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                            <b>3.7.</b>
                        
                        第7节：文件操作
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                            <b>3.8.</b>
                        
                        第8节：数据清洗
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                            <b>3.9.</b>
                        
                        第9节：聚合和分组
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                            <b>3.10.</b>
                        
                        第10节：时间序列
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                            <b>4.</b>
                        
                        第四章：Matploblib库
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                            <b>4.1.</b>
                        
                        第1节：常用图
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                            <b>4.2.</b>
                        
                        第2节：基本使用
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                            <b>4.3.</b>
                        
                        第3节：条形图
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                            <b>4.4.</b>
                        
                        第4节：直方图
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                            <b>4.5.</b>
                        
                        第5节：散点图
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                            <b>4.6.</b>
                        
                        第6节：饼图
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                            <b>4.7.</b>
                        
                        第7节：箱线图
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                            <b>4.8.</b>
                        
                        第8节：雷达图
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                            <b>4.9.</b>
                        
                        第9节：matplotlib绘图分析
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                            <b>4.10.</b>
                        
                        第10节：多图布局
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                            <b>4.11.</b>
                        
                        第11节：matplotlib配置
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                            <b>4.12.</b>
                        
                        第12节：matplotlib作业
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                            <b>5.</b>
                        
                        第五章：Seaborn库
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                            <b>5.1.</b>
                        
                        第1节：关系绘图
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                            <b>5.2.</b>
                        
                        第2节：分类绘图
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                            <b>5.3.</b>
                        
                        第3节：分布绘图
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                            <b>5.4.</b>
                        
                        第4节：线性关系绘图
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                            <b>5.5.</b>
                        
                        第5节：FacetGrid结构图
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                            <b>5.6.</b>
                        
                        第6节：样式设置
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                            <b>5.7.</b>
                        
                        第7节：调色盘设置
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                            <b>5.8.</b>
                        
                        第8节：seaborn作业
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                            <b>6.</b>
                        
                        第六章：统计分析强化
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                            <b>6.1.</b>
                        
                        第1节：常用专业数学术语
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                            <b>6.2.</b>
                        
                        第2节：平均数
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                            <b>6.3.</b>
                        
                        第3节：方差和标准差
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                            <b>6.4.</b>
                        
                        第4节：正态分布
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                            <b>6.5.</b>
                        
                        第5节：对比分析
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                            <b>6.6.</b>
                        
                        第6节：分布分析
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                            <b>6.7.</b>
                        
                        第7节：交叉分析
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                            <b>6.8.</b>
                        
                        第8节：统计分析
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                            <b>6.9.</b>
                        
                        第9节：帕累托分析
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                            <b>6.10.</b>
                        
                        第10节：矩阵关联分析
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                            <b>6.11.</b>
                        
                        第11节：综合性分析
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                        第七章：数据分析实战
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                            <b>7.1.</b>
                        
                        第1节：App数据分析实战
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                            <b>7.2.</b>
                        
                        第2节：心脏病患者数据分析
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                            <b>7.3.</b>
                        
                        第3节：StackoverFlow数据分析
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                            <b>7.4.</b>
                        
                        第4节：二手房数据分析
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                            <b>7.5.</b>
                        
                        第5节：吃鸡数据分析
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                            <b>7.6.</b>
                        
                        第6节：黑色星期五数据分析
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                            <b>8.</b>
                        
                        第八章：补充
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                            <b>8.1.</b>
                        
                        第1节：用Excel做数据分析
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                            <b>8.2.</b>
                        
                        第2节：echarts和pyecharts库
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                        第3节：bokeh库
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                        <h1 id="&#x76F4;&#x65B9;&#x56FE;">&#x76F4;&#x65B9;&#x56FE;</h1>
<p>&#x76F4;&#x65B9;&#x56FE;(Histogram)&#xFF0C;&#x53C8;&#x79F0;&#x8D28;&#x91CF;&#x5206;&#x5E03;&#x56FE;&#xFF0C;&#x662F;&#x4E00;&#x79CD;&#x7EDF;&#x8BA1;&#x62A5;&#x544A;&#x56FE;&#xFF0C;&#x7531;&#x4E00;&#x7CFB;&#x5217;&#x9AD8;&#x5EA6;&#x4E0D;&#x7B49;&#x7684;&#x6761;&#x7EB9;&#x8868;&#x793A;&#x6570;&#x636E;&#x5206;&#x5E03;&#x7684;&#x60C5;&#x51B5;&#x3002;&#x4E00;&#x822C;&#x7528;&#x6A2A;&#x8F74;&#x8868;&#x793A;&#x6570;&#x636E;&#x7C7B;&#x578B;&#xFF0C;&#x7EB5;&#x8F74;&#x8868;&#x793A;&#x5206;&#x5E03;&#x60C5;&#x51B5;&#x3002;<br>&#x76F4;&#x65B9;&#x56FE;&#x662F;&#x6570;&#x503C;&#x6570;&#x636E;&#x5206;&#x5E03;&#x7684;&#x7CBE;&#x786E;&#x56FE;&#x5F62;&#x8868;&#x793A;&#x3002;&#x4E3A;&#x4E86;&#x6784;&#x5EFA;&#x76F4;&#x65B9;&#x56FE;&#xFF0C;&#x7B2C;&#x4E00;&#x6B65;&#x662F;&#x5C06;&#x503C;&#x7684;&#x8303;&#x56F4;&#x5206;&#x6BB5;&#xFF0C;&#x5373;&#x5C06;&#x6574;&#x4E2A;&#x503C;&#x7684;&#x8303;&#x56F4;&#x5206;&#x6210;&#x4E00;&#x7CFB;&#x5217;&#x95F4;&#x9694;&#xFF0C;&#x7136;&#x540E;&#x8BA1;&#x7B97;&#x6BCF;&#x4E2A;&#x95F4;&#x9694;&#x4E2D;&#x6709;&#x591A;&#x5C11;&#x503C;&#x3002;&#x8FD9;&#x4E9B;&#x503C;&#x901A;&#x5E38;&#x88AB;&#x6307;&#x5B9A;&#x4E3A;&#x8FDE;&#x7EED;&#x7684;&#xFF0C;&#x4E0D;&#x91CD;&#x53E0;&#x7684;&#x53D8;&#x91CF;&#x95F4;&#x9694;&#x3002;&#x95F4;&#x9694;&#x5FC5;&#x987B;&#x76F8;&#x90BB;&#xFF0C;&#x5E76;&#x4E14;&#x901A;&#x5E38;&#x662F;&#xFF08;&#x4F46;&#x4E0D;&#x662F;&#x5FC5;&#x987B;&#x7684;&#xFF09;&#x76F8;&#x7B49;&#x7684;&#x5927;&#x5C0F;&#x3002;</p>
<h2 id="&#x7ED8;&#x5236;&#x76F4;&#x65B9;&#x56FE;&#xFF1A;">&#x7ED8;&#x5236;&#x76F4;&#x65B9;&#x56FE;&#xFF1A;</h2>
<p>&#x76F4;&#x65B9;&#x56FE;&#x7684;&#x7ED8;&#x5236;&#x65B9;&#x6CD5;&#xFF0C;&#x4F7F;&#x7528;&#x7684;&#x662F;<code>plt.hist</code>&#x65B9;&#x6CD5;&#x6765;&#x5B9E;&#x73B0;&#xFF0C;&#x8FD9;&#x4E2A;&#x65B9;&#x6CD5;&#x7684;&#x53C2;&#x6570;&#x4EE5;&#x53CA;&#x8FD4;&#x56DE;&#x503C;&#x5982;&#x4E0B;&#xFF1A;</p>
<p><strong> &#x53C2;&#x6570;&#xFF1A; </strong></p>
<ol>
<li><code>x</code>&#xFF1A;&#x6570;&#x7EC4;&#x6216;&#x8005;&#x53EF;&#x4EE5;&#x5FAA;&#x73AF;&#x7684;&#x5E8F;&#x5217;&#x3002;&#x76F4;&#x65B9;&#x56FE;&#x5C06;&#x4F1A;&#x4ECE;&#x8FD9;&#x7EC4;&#x6570;&#x636E;&#x4E2D;&#x8FDB;&#x884C;&#x5206;&#x7EC4;&#x3002;</li>
<li><code>bins</code>&#xFF1A;&#x6570;&#x5B57;&#x6216;&#x8005;&#x5E8F;&#x5217;&#xFF08;&#x6570;&#x7EC4;/&#x5217;&#x8868;&#x7B49;&#xFF09;&#x3002;&#x5982;&#x679C;&#x662F;&#x6570;&#x5B57;&#xFF0C;&#x4EE3;&#x8868;&#x7684;&#x662F;&#x8981;&#x5206;&#x6210;&#x591A;&#x5C11;&#x7EC4;&#x3002;&#x5982;&#x679C;&#x662F;&#x5E8F;&#x5217;&#xFF0C;&#x90A3;&#x4E48;&#x5C31;&#x4F1A;&#x6309;&#x7167;&#x5E8F;&#x5217;&#x4E2D;&#x6307;&#x5B9A;&#x7684;&#x503C;&#x8FDB;&#x884C;&#x5206;&#x7EC4;&#x3002;&#x6BD4;&#x5982;<code>[1,2,3,4]</code>&#xFF0C;&#x90A3;&#x4E48;&#x5206;&#x7EC4;&#x7684;&#x65F6;&#x5019;&#x4F1A;&#x6309;&#x7167;&#x4E09;&#x4E2A;&#x533A;&#x95F4;&#x5206;&#x6210;3&#x7EC4;&#xFF0C;&#x5206;&#x522B;&#x662F;<code>[1,2)/[2,3)/[3,4]</code>&#x3002;</li>
<li><code>range</code>&#xFF1A;&#x5143;&#x7EC4;&#x6216;&#x8005;None&#xFF0C;&#x5982;&#x679C;&#x4E3A;&#x5143;&#x7EC4;&#xFF0C;&#x90A3;&#x4E48;&#x6307;&#x5B9A;<code>x</code>&#x5212;&#x5206;&#x533A;&#x95F4;&#x7684;&#x6700;&#x5927;&#x503C;&#x548C;&#x6700;&#x5C0F;&#x503C;&#x3002;&#x5982;&#x679C;<code>bins</code>&#x662F;&#x4E00;&#x4E2A;&#x5E8F;&#x5217;&#xFF0C;&#x90A3;&#x4E48;<code>range</code>&#x6CA1;&#x6709;&#x6709;&#x6CA1;&#x6709;&#x8BBE;&#x7F6E;&#x6CA1;&#x6709;&#x4EFB;&#x4F55;&#x5F71;&#x54CD;&#x3002;</li>
<li><code>density</code>&#xFF1A;&#x9ED8;&#x8BA4;&#x662F;<code>False</code>&#xFF0C;&#x5982;&#x679C;&#x7B49;&#x4E8E;<code>True</code>&#xFF0C;&#x90A3;&#x4E48;&#x5C06;&#x4F1A;&#x4F7F;&#x7528;&#x9891;&#x7387;&#x5206;&#x5E03;&#x76F4;&#x65B9;&#x56FE;&#x3002;&#x6BCF;&#x4E2A;&#x6761;&#x5F62;&#x8868;&#x793A;&#x7684;&#x4E0D;&#x662F;&#x4E2A;&#x6570;&#xFF0C;&#x800C;&#x662F;<code>&#x9891;&#x7387;/&#x7EC4;&#x8DDD;</code>&#xFF08;&#x843D;&#x5728;&#x5404;&#x7EC4;&#x6837;&#x672C;&#x6570;&#x636E;&#x7684;&#x4E2A;&#x6570;&#x79F0;&#x4E3A;&#x9891;&#x6570;&#xFF0C;&#x9891;&#x6570;&#x9664;&#x4EE5;&#x6837;&#x672C;&#x603B;&#x4E2A;&#x6570;&#x4E3A;&#x9891;&#x7387;&#xFF09;&#x3002;</li>
<li><code>cumulative</code>&#xFF1A;&#x5982;&#x679C;&#x8FD9;&#x4E2A;&#x548C;<code>density</code>&#x90FD;&#x7B49;&#x4E8E;<code>True</code>&#xFF0C;&#x90A3;&#x4E48;&#x8FD4;&#x56DE;&#x503C;&#x7684;&#x7B2C;&#x4E00;&#x4E2A;&#x53C2;&#x6570;&#x4F1A;&#x4E0D;&#x65AD;&#x7684;&#x7D2F;&#x52A0;&#xFF0C;&#x6700;&#x7EC8;&#x7B49;&#x4E8E;<code>1</code>&#x3002;</li>
<li>&#x5176;&#x4ED6;&#x53C2;&#x6570;&#xFF1A;&#x8BF7;&#x53C2;&#x8003;&#xFF1A;<code>https://matplotlib.org/api/_as_gen/matplotlib.pyplot.hist.html</code>&#x3002;</li>
</ol>
<p><strong>&#x8FD4;&#x56DE;&#x503C;&#xFF1A;</strong></p>
<ol>
<li><code>n</code>&#xFF1A;&#x6570;&#x7EC4;&#x3002;&#x6BCF;&#x4E2A;&#x533A;&#x95F4;&#x5185;&#x503C;&#x51FA;&#x73B0;&#x7684;&#x4E2A;&#x6570;&#xFF0C;&#x5982;&#x679C;<code>density=True</code>&#xFF0C;&#x90A3;&#x4E48;&#x8FD9;&#x4E2A;&#x5C06;&#x8FD4;&#x56DE;&#x7684;&#x662F;<code>&#x9891;&#x7387;/&#x7EC4;&#x8DDD;</code>&#x3002;</li>
<li><code>bins</code>&#xFF1A;&#x6570;&#x7EC4;&#x3002;&#x533A;&#x95F4;&#x7684;&#x503C;&#x3002;</li>
<li><code>patches</code>&#xFF1A;&#x6570;&#x7EC4;&#x3002;&#x6BCF;&#x6839;&#x6761;&#x7684;&#x5BF9;&#x8C61;&#xFF0C;&#x7C7B;&#x578B;&#x662F;<code>matplotlib.patches.Rectangle</code>&#x3002;</li>
</ol>
<h2 id="&#x6848;&#x4F8B;&#xFF1A;">&#x6848;&#x4F8B;&#xFF1A;</h2>
<p>&#x6BD4;&#x5982;&#x6709;&#x4E00;&#x7EC4;&#x7535;&#x5F71;&#x7968;&#x623F;&#x65F6;&#x957F;&#xFF0C;&#x60F3;&#x8981;&#x770B;&#x4E0B;&#x8FD9;&#x7EC4;&#x7968;&#x623F;&#x65F6;&#x957F;&#x7684;&#x6570;&#x636E;&#xFF0C;&#x90A3;&#x4E48;&#x53EF;&#x4EE5;&#x901A;&#x8FC7;&#x4EE5;&#x4E0B;&#x4EE3;&#x7801;&#x6765;&#x5B9E;&#x73B0;&#xFF1A;</p>
<pre><code class="lang-python">durations = [<span class="hljs-number">131</span>,  <span class="hljs-number">98</span>, <span class="hljs-number">125</span>, <span class="hljs-number">131</span>, <span class="hljs-number">124</span>, <span class="hljs-number">139</span>, <span class="hljs-number">131</span>, <span class="hljs-number">117</span>, <span class="hljs-number">128</span>, <span class="hljs-number">108</span>, <span class="hljs-number">135</span>, <span class="hljs-number">138</span>, <span class="hljs-number">131</span>, <span class="hljs-number">102</span>, <span class="hljs-number">107</span>, <span class="hljs-number">114</span>, <span class="hljs-number">119</span>, <span class="hljs-number">128</span>, <span class="hljs-number">121</span>, <span class="hljs-number">142</span>, <span class="hljs-number">127</span>, <span class="hljs-number">130</span>, <span class="hljs-number">124</span>, <span class="hljs-number">101</span>, <span class="hljs-number">110</span>, <span class="hljs-number">116</span>, <span class="hljs-number">117</span>, <span class="hljs-number">110</span>, <span class="hljs-number">128</span>, <span class="hljs-number">128</span>, <span class="hljs-number">115</span>,  <span class="hljs-number">99</span>, <span class="hljs-number">136</span>, <span class="hljs-number">126</span>, <span class="hljs-number">134</span>,  <span class="hljs-number">95</span>, <span class="hljs-number">138</span>, <span class="hljs-number">117</span>, <span class="hljs-number">111</span>,<span class="hljs-number">78</span>, <span class="hljs-number">132</span>, <span class="hljs-number">124</span>, <span class="hljs-number">113</span>, <span class="hljs-number">150</span>, <span class="hljs-number">110</span>, <span class="hljs-number">117</span>,  <span class="hljs-number">86</span>,  <span class="hljs-number">95</span>, <span class="hljs-number">144</span>, <span class="hljs-number">105</span>, <span class="hljs-number">126</span>, <span class="hljs-number">130</span>,<span class="hljs-number">126</span>, <span class="hljs-number">130</span>, <span class="hljs-number">126</span>, <span class="hljs-number">116</span>, <span class="hljs-number">123</span>, <span class="hljs-number">106</span>, <span class="hljs-number">112</span>, <span class="hljs-number">138</span>, <span class="hljs-number">123</span>,  <span class="hljs-number">86</span>, <span class="hljs-number">101</span>,  <span class="hljs-number">99</span>, <span class="hljs-number">136</span>,<span class="hljs-number">123</span>, <span class="hljs-number">117</span>, <span class="hljs-number">119</span>, <span class="hljs-number">105</span>, <span class="hljs-number">137</span>, <span class="hljs-number">123</span>, <span class="hljs-number">128</span>, <span class="hljs-number">125</span>, <span class="hljs-number">104</span>, <span class="hljs-number">109</span>, <span class="hljs-number">134</span>, <span class="hljs-number">125</span>, <span class="hljs-number">127</span>,<span class="hljs-number">105</span>, <span class="hljs-number">120</span>, <span class="hljs-number">107</span>, <span class="hljs-number">129</span>, <span class="hljs-number">116</span>, <span class="hljs-number">108</span>, <span class="hljs-number">132</span>, <span class="hljs-number">103</span>, <span class="hljs-number">136</span>, <span class="hljs-number">118</span>, <span class="hljs-number">102</span>, <span class="hljs-number">120</span>, <span class="hljs-number">114</span>,<span class="hljs-number">105</span>, <span class="hljs-number">115</span>, <span class="hljs-number">132</span>, <span class="hljs-number">145</span>, <span class="hljs-number">119</span>, <span class="hljs-number">121</span>, <span class="hljs-number">112</span>, <span class="hljs-number">139</span>, <span class="hljs-number">125</span>, <span class="hljs-number">138</span>, <span class="hljs-number">109</span>, <span class="hljs-number">132</span>, <span class="hljs-number">134</span>,<span class="hljs-number">156</span>, <span class="hljs-number">106</span>, <span class="hljs-number">117</span>, <span class="hljs-number">127</span>, <span class="hljs-number">144</span>, <span class="hljs-number">139</span>, <span class="hljs-number">139</span>, <span class="hljs-number">119</span>, <span class="hljs-number">140</span>,  <span class="hljs-number">83</span>, <span class="hljs-number">110</span>, <span class="hljs-number">102</span>,<span class="hljs-number">123</span>,<span class="hljs-number">107</span>, <span class="hljs-number">143</span>, <span class="hljs-number">115</span>, <span class="hljs-number">136</span>, <span class="hljs-number">118</span>, <span class="hljs-number">139</span>, <span class="hljs-number">123</span>, <span class="hljs-number">112</span>, <span class="hljs-number">118</span>, <span class="hljs-number">125</span>, <span class="hljs-number">109</span>, <span class="hljs-number">119</span>, <span class="hljs-number">133</span>,<span class="hljs-number">112</span>, <span class="hljs-number">114</span>, <span class="hljs-number">122</span>, <span class="hljs-number">109</span>, <span class="hljs-number">106</span>, <span class="hljs-number">123</span>, <span class="hljs-number">116</span>, <span class="hljs-number">131</span>, <span class="hljs-number">127</span>, <span class="hljs-number">115</span>, <span class="hljs-number">118</span>, <span class="hljs-number">112</span>, <span class="hljs-number">135</span>,<span class="hljs-number">115</span>, <span class="hljs-number">146</span>, <span class="hljs-number">137</span>, <span class="hljs-number">116</span>, <span class="hljs-number">103</span>, <span class="hljs-number">144</span>,  <span class="hljs-number">83</span>, <span class="hljs-number">123</span>, <span class="hljs-number">111</span>, <span class="hljs-number">110</span>, <span class="hljs-number">111</span>, <span class="hljs-number">100</span>, <span class="hljs-number">154</span>,<span class="hljs-number">136</span>, <span class="hljs-number">100</span>, <span class="hljs-number">118</span>, <span class="hljs-number">119</span>, <span class="hljs-number">133</span>, <span class="hljs-number">134</span>, <span class="hljs-number">106</span>, <span class="hljs-number">129</span>, <span class="hljs-number">126</span>, <span class="hljs-number">110</span>, <span class="hljs-number">111</span>, <span class="hljs-number">109</span>, <span class="hljs-number">141</span>,<span class="hljs-number">120</span>, <span class="hljs-number">117</span>, <span class="hljs-number">106</span>, <span class="hljs-number">149</span>, <span class="hljs-number">122</span>, <span class="hljs-number">122</span>, <span class="hljs-number">110</span>, <span class="hljs-number">118</span>, <span class="hljs-number">127</span>, <span class="hljs-number">121</span>, <span class="hljs-number">114</span>, <span class="hljs-number">125</span>, <span class="hljs-number">126</span>,<span class="hljs-number">114</span>, <span class="hljs-number">140</span>, <span class="hljs-number">103</span>, <span class="hljs-number">130</span>, <span class="hljs-number">141</span>, <span class="hljs-number">117</span>, <span class="hljs-number">106</span>, <span class="hljs-number">114</span>, <span class="hljs-number">121</span>, <span class="hljs-number">114</span>, <span class="hljs-number">133</span>, <span class="hljs-number">137</span>,  <span class="hljs-number">92</span>,<span class="hljs-number">121</span>, <span class="hljs-number">112</span>, <span class="hljs-number">146</span>,  <span class="hljs-number">97</span>, <span class="hljs-number">137</span>, <span class="hljs-number">105</span>,  <span class="hljs-number">98</span>, <span class="hljs-number">117</span>, <span class="hljs-number">112</span>,  <span class="hljs-number">81</span>,  <span class="hljs-number">97</span>, <span class="hljs-number">139</span>, <span class="hljs-number">113</span>,<span class="hljs-number">134</span>, <span class="hljs-number">106</span>, <span class="hljs-number">144</span>, <span class="hljs-number">110</span>, <span class="hljs-number">137</span>, <span class="hljs-number">137</span>, <span class="hljs-number">111</span>, <span class="hljs-number">104</span>, <span class="hljs-number">117</span>, <span class="hljs-number">100</span>, <span class="hljs-number">111</span>, <span class="hljs-number">101</span>, <span class="hljs-number">110</span>,<span class="hljs-number">105</span>, <span class="hljs-number">129</span>, <span class="hljs-number">137</span>, <span class="hljs-number">112</span>, <span class="hljs-number">120</span>, <span class="hljs-number">113</span>, <span class="hljs-number">133</span>, <span class="hljs-number">112</span>,  <span class="hljs-number">83</span>,  <span class="hljs-number">94</span>, <span class="hljs-number">146</span>, <span class="hljs-number">133</span>, <span class="hljs-number">101</span>,<span class="hljs-number">131</span>, <span class="hljs-number">116</span>, <span class="hljs-number">111</span>,  <span class="hljs-number">84</span>, <span class="hljs-number">137</span>, <span class="hljs-number">115</span>, <span class="hljs-number">122</span>, <span class="hljs-number">106</span>, <span class="hljs-number">144</span>, <span class="hljs-number">109</span>, <span class="hljs-number">123</span>, <span class="hljs-number">116</span>, <span class="hljs-number">111</span>,<span class="hljs-number">111</span>, <span class="hljs-number">133</span>, <span class="hljs-number">150</span>]
plt.figure(figsize=(<span class="hljs-number">15</span>,<span class="hljs-number">5</span>))
nums,bins,patches = plt.hist(durations,bins=<span class="hljs-number">20</span>,edgecolor=<span class="hljs-string">&apos;k&apos;</span>)
plt.xticks(bins,bins)
<span class="hljs-keyword">for</span> num,bin <span class="hljs-keyword">in</span> zip(nums,bins):
    plt.annotate(num,xy=(bin,num),xytext=(bin+<span class="hljs-number">1.5</span>,num+<span class="hljs-number">0.5</span>))
plt.show()
</code></pre>
<p>&#x6548;&#x679C;&#x56FE;&#x5982;&#x4E0B;&#xFF1A;<br><img src="../assets/chapter04/&#x7535;&#x5F71;&#x65F6;&#x95F4;&#x76F4;&#x65B9;&#x56FE;.png" alt=""></p>
<p>&#x53E6;&#x5916;&#xFF0C;&#x4E5F;&#x53EF;&#x4EE5;&#x901A;&#x8FC7;<code>density=True</code>&#xFF0C;&#x6765;&#x5B9E;&#x73B0;&#x9891;&#x7387;&#x5206;&#x5E03;&#x76F4;&#x65B9;&#x56FE;&#x3002;&#x793A;&#x4F8B;&#x4EE3;&#x7801;&#x5982;&#x4E0B;&#xFF1A;</p>
<pre><code class="lang-python">nums,bins,patches = plt.hist(durations,bins=<span class="hljs-number">20</span>,edgecolor=<span class="hljs-string">&apos;k&apos;</span>,density=<span class="hljs-keyword">True</span>)
plt.xticks(bins,bins)
<span class="hljs-keyword">for</span> num,bin <span class="hljs-keyword">in</span> zip(nums,bins):
    plt.annotate(<span class="hljs-string">&quot;%.4f&quot;</span>%num,xy=(bin,num),xytext=(bin+<span class="hljs-number">0.2</span>,num+<span class="hljs-number">0.0005</span>))
</code></pre>
<p><img src="../assets/chapter04/&#x7535;&#x5F71;&#x9891;&#x7387;&#x76F4;&#x65B9;&#x56FE;.png" alt=""></p>
<p>&#x800C;&#x5982;&#x679C;&#x60F3;&#x8981;&#x8BA9;<code>nums</code>&#x7684;&#x603B;&#x548C;&#x4E3A;<code>1</code>&#xFF0C;&#x90A3;&#x4E48;&#x5C31;&#x9700;&#x8981;&#x8BBE;&#x7F6E;<code>cumulative=True</code>&#x53C2;&#x6570;&#xFF0C;&#x793A;&#x4F8B;&#x4EE3;&#x7801;&#x5982;&#x4E0B;&#xFF1A;</p>
<pre><code class="lang-python">nums,bins,patches = plt.hist(durations,bins=<span class="hljs-number">20</span>,edgecolor=<span class="hljs-string">&apos;k&apos;</span>,density=<span class="hljs-keyword">True</span>,cumulative=<span class="hljs-keyword">True</span>)
plt.xticks(bins,bins)
<span class="hljs-keyword">for</span> num,bin <span class="hljs-keyword">in</span> zip(nums,bins):
    plt.annotate(<span class="hljs-string">&quot;%.4f&quot;</span>%num,xy=(bin,num),xytext=(bin+<span class="hljs-number">0.2</span>,num+<span class="hljs-number">0.0005</span>))
</code></pre>
<h2 id="&#x76F4;&#x65B9;&#x56FE;&#x7684;&#x5E94;&#x7528;&#x573A;&#x666F;&#xFF1A;">&#x76F4;&#x65B9;&#x56FE;&#x7684;&#x5E94;&#x7528;&#x573A;&#x666F;&#xFF1A;</h2>
<ol>
<li>&#x663E;&#x793A;&#x5404;&#x7EC4;&#x6570;&#x636E;&#x6570;&#x91CF;&#x5206;&#x5E03;&#x7684;&#x60C5;&#x51B5;&#x3002;</li>
<li>&#x7528;&#x4E8E;&#x89C2;&#x5BDF;&#x5F02;&#x5E38;&#x6216;&#x5B64;&#x7ACB;&#x6570;&#x636E;&#x3002;</li>
<li>&#x62BD;&#x53D6;&#x7684;&#x6837;&#x672C;&#x6570;&#x91CF;&#x8FC7;&#x5C0F;&#xFF0C;&#x5C06;&#x4F1A;&#x4EA7;&#x751F;&#x8F83;&#x5927;&#x8BEF;&#x5DEE;&#xFF0C;&#x53EF;&#x4FE1;&#x5EA6;&#x4F4E;&#xFF0C;&#x4E5F;&#x5C31;&#x5931;&#x53BB;&#x4E86;&#x7EDF;&#x8BA1;&#x7684;&#x610F;&#x4E49;&#x3002;&#x56E0;&#x6B64;&#xFF0C;&#x6837;&#x672C;&#x6570;&#x4E0D;&#x5E94;&#x5C11;&#x4E8E;50&#x4E2A;&#x3002;</li>
</ol>

                    
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